Inference of large modified Poisson-type graphical models: application to RNA-seq data in childhood atopic asthma studies
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Publication:2245161
DOI10.1214/20-AOAS1413zbMath1478.62350OpenAlexW3181280211MaRDI QIDQ2245161
Wei Chen, Juan C. Celedón, Zhao Ren, Rong Zhang
Publication date: 15 November 2021
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/20-aoas1413
asymptotic normalitymultiple testingbias correctionRNA-seq dataPoisson graphical modelasthma genomics
Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20) Probabilistic graphical models (62H22)
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- On asymptotically optimal confidence regions and tests for high-dimensional models
- Confidence intervals for high-dimensional inverse covariance estimation
- Asymptotic normality and optimalities in estimation of large Gaussian graphical models
- Honest confidence regions and optimality in high-dimensional precision matrix estimation
- Gaussian graphical model estimation with false discovery rate control
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- High-dimensional semiparametric Gaussian copula graphical models
- High-dimensional Ising model selection with Bayesian information criteria
- A statistical framework for data integration through graphical models with application to cancer genomics
- On Graphical Models via Univariate Exponential Family Distributions
- Emergence of Scaling in Random Networks
- Learning gene regulatory networks from next generation sequencing data
- Differential Markov random field analysis with an application to detecting differential microbial community networks
- The huge Package for High-dimensional Undirected Graph Estimation in R
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
